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. 2022 Aug 30:10:28-30.
doi: 10.1016/j.jdin.2022.08.014. eCollection 2023 Mar.

The impact of stage-related features in melanoma recurrence prediction: A machine learning approach

Guihong Wan  1   2   3 , Bonnie Leung  1 , Nga Nguyen  1 , Mia S DeSimone  4 , Feng Liu  5 , Min Seok Choi  1 , Diane Ho  1 , Valerie Laucks  1 , Stacey Duey  1 , Ryan J Sullivan  6 , Genevieve M Boland  7 , Nicole R LeBoeuf  8 , David Liu  9 , Alexander Gusev  9 , Shawn G Kwatra  10 , Peter K Sorger  3 , Kun-Hsing Yu  2   5 , Yevgeniy R Semenov  1   3
Affiliations

The impact of stage-related features in melanoma recurrence prediction: A machine learning approach

Guihong Wan et al. JAAD Int. .
No abstract available

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Conflict of interest statement

Y.R.S. is an advisory board member/consultant and has received honoraria from Incyte Corporation, Castle Biosciences, Galderma, and Sanofi outside of the submitted work.

Figures

None
Graphical abstract
Fig 1
Fig 1
The ranked average feature importance in the recurrence versus nonrecurrence prediction by the 3 machine learning models. The experiments were conducted on the original cohort. Categorical features were converted by one-hot encoding. Features with zero importance were ignored. All extracted features were presented for the random forest model. AUC, Area under the receiver operating characteristic curve; CI, confidence interval.
See this image and copyright information in PMC

References

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